A reliable noninvasive imaging modality is useful for evaluating and monitoring patients with cardiac disease. Traditional screening techniques for quantitative assessment of cardiac function include electromagnetic, acoustic, nuclear, and other modalities.
Multi-Gated Angiography (MUGA) is a slow screening modality that requires the injection of a radiopharmaceutical agent by a clinician. MUGA screening examines the pumping function of the heart. After injection of a radioactive agent that labels red blood cells, a nuclear camera creates an image of the heart's chambers by counting these cells over numerous cardiac cycles. The information obtained can quantify ejection fraction, but not ventricular volumes.
Magnetic Resonance Imaging (MRI) has been largely limited to university hospitals where there is a strong interest in research, despite the common availability of MRI machines in the United States. MRI is useful in evaluating patients' cardiac anatomy prior to surgery, in locating and characterizing cardiac tumors, and in identifying and treating cardiac abnormalities for children with complex congenital heart disease. These clinical situations are relatively rare and cardiac MRI has yet to become a commonly used tool in clinical medicine.
Computerized Tomography (CT) multi-detector technology has made cardiac CT possible, enabling angiography, perfusion and function studies. The main limitation of this screening modality remains the acquisition time with multi-row detectors, which motivates the use of new spiral CT, electron beam CT and ultrafast CT technologies.
Single Photon Emission Computed Tomography (SPECT), also referred to as myocardial perfusion imaging, is used to visualize myocardial blood flow distribution by intravenous injection of a radionuclide detected by a crystal gamma camera rotating around the patient's body. SPECT can be used to assess ejection fraction and regional wall motion but cannot provide detailed views of anatomical structures.
Positive Emission Tomography (PET) visualizes myocardial blood flow using intravenous injection of positron-emitting tracers detected by multiple rings of stationary detectors encircling the patient's body to produce a series of tomographic images of the heart. Specific tracers have been developed for the evaluation and quantification of a number of physiological processes, including regional myocardial blood flow, metabolic processes, oxygen consumption, receptor activity, and membrane function. Compared to SPECT, PET images are usually more accurate in clinical studies, but PET scanners remain costly and therefore less widely available than SPECT systems.
Two-dimensional Echocardiography (2DE) is relatively fast, cheap, and less invasive as a screening modality for imaging the heart. Because of the three-dimensional structure and deformation of the heart muscle during the cardiac cycle, analysis of irregularly-shaped cardiac chambers or description of valve morphology using 2D images can be challenging. Also, because 2DE is constrained to planar views, highly-trained clinicians are usually required to perform the screening. Screening examination protocols include the acquisition of multiple views of the heart at distinct phases of the cardiac cycle. This procedure is time consuming and does not guarantee that the entire cardiac volume and its important structures have been included in the acquired data. This can lead to situations where a patient may need to be screened a second time to acquire the information missing from the original exam.
Early nonlinear methods performed adaptive image filtering inside identified homogeneous regions, using statistical filters derived from synthetic aperture radar (SAR) imaging. Some filters were derived from weighted median filters, reassigning pixel values based on local averaging through a moving window.
A second class of filter denoising techniques consists of enhancing edges and contours rather than smoothing homogeneous areas. With this approach, geometric templates are applied as a set of filter banks and each point in the image is associated with the largest filter output, as a test statistic.
Some work was also done in the area of deconvolution, and has focused on the estimation of pulse-echo sequences from an original radio frequency (RF) signal acquired during tissue scanning. Denoising using nonlinear estimators models an ultrasound signal as the sum of a “true” signal component and an additive noise component. Estimation of the true signal component is performed using expansion of the acquired signal onto a set of basis functions and elimination of the noise components in the transform domain.
Modern imaging applications, and especially medical imaging, increasingly relies on three-dimensional image data obtained using sophisticated imaging arrays. Medical imaging systems also commonly incorporate temporal information as well as spatial information, e.g., in obtaining animated or time-sequence data with imaging and diagnostic devices. Therefore, new information (multi-dimensional and time-dependent information) is now available and can be used in mitigating the effects of noise on image quality, especially where a continuity of such information in time or space can be exploited.
Noise, such as speckle noise degrades the quality of medical images. In the field of medical image processing in general, and for ultrasound denoising in particular, denoising using wavelet expansion has been used for separating signal and noise components using nonlinear estimators or deconvolution paradigms. Both of these methods are performed using selective reconstruction of a “noise-free” signal by thresholding wavelet coefficients. Speckle noise characteristics depend on several parameters, including the ultrasound hardware design, acquisition protocol, orientation of the transducer during examination, and the acoustic window available in the patient.
Brushlet analysis was introduced around 1997 for the compression of images having directional texture, but not for removing noise from such images. An optimized system and method for analyzing images containing texture features and noise, e.g. medical images, is presently lacking, especially for multi-dimensional and time-varying images. Also, a description of favorable or best practices for applying image analysis processing steps to noisy images is also lacking in existing systems.